import streamlit as st
import pinecone
from makechain import get_chain
from langchain.vectorstores.pinecone import Pinecone
from env import PINECONE_INDEX_NAME, PINECONE_ENVIRONMENT, PINECONE_API_KEY, OPENAI_API_KEY
from langchain.embeddings.openai import OpenAIEmbeddings


st.title("Ask the Black@Stanford Exhibit")
st.sidebar.header("You can ask questions of interviews with Black Stanford students and faculty from the University "
                  "Archives")
st.sidebar.info(
    '''This is a web application that allows you to interact with 
       the Stanford Archives.
       Enter a **Question** in the **text box** and **press enter** to receive 
       a **response** from our ChatBot.
       '''
)

# create Vectorstore
pinecone.init(
    api_key=st.secrets["PINECONE_API_KEY"],  # find at app.pinecone.io
    environment=st.secrets["PINECONE_ENVIRONMENT"]  # next to api key in console
)
index = pinecone.Index(index_name=st.secrets["PINECONE_INDEX_NAME"])
embed = OpenAIEmbeddings(openai_api_key=st.secrets["OPENAI_API_KEY"])
text_field = "text"
vectorStore = Pinecone(
    index, embed.embed_query, text_field
)

# create chain
qa_chain = get_chain(vectorStore)

def main():
    global query

    user_query= st.text_input("Enter your question here")
    if user_query != ":q" or user_query != "":
        # Pass the query to the ChatGPT function
        query = user_query.strip().replace('\n', ' ')
        response = qa_chain(
            {
                'question': query,
            }
        )
        st.write(f"{response['answer']}")
        st.write("Sources: ")
        st.write(f"{response['sources']}")

try:
    main()
except Exception as e:
    st.write("An error occurred while running the application: ", e)